Skip to main content
SpringerLink
Log in

Clothing invariant human gait recognition using modified local optimal oriented pattern binary descriptor

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Human gait is a behavioral characteristic which has received a large amount of consideration in recent times as a biometric identifier. The clothing variance is one of the most common covariate influences which can influence the performance of gait recognition approach in real-world scenarios. This paper proposes a gait recognition approach proficient in choosing information characteristics for individual identification under different clothing conditions. The proposed method constitutes of addressing the feature extraction technique by introducing a binary descriptor called as Modified Local Optimal Oriented Pattern (MLOOP). In the proposed approach, initially, the feature vectors such as histogram and horizontal width vector are extracted from MLOOP descriptor, and then the dimensionality of the feature vector is reduced to remove the irrelevant features. The performance of MLOOP was accessed against its predecessors. Obtained experimental results demonstrate that the MLOOP descriptor performs better than the previous binary descriptors. Furthermore, the performance analysis of the proposed approach was assessed on OU-ISIR B treadmill gait database and CASIA B gait database. Broad investigations demonstrate the viability of the proposed technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Alotaibi M, Mahmood A (2017) Improved gait recognition based on specialized deep convolutional neural network. Comput Vis Image Underst 164:103–110

    Article  Google Scholar 

  2. Bashir K, Xiang T, Gong S, Mary Q (2009) Gait representation using flow fields. In: BMVC, pp 1–11

  3. Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31(13):2052–2060

    Article  Google Scholar 

  4. Boulgouris NV, Plataniotis KN, Hatzinakos D (2006) Gait recognition using linear time normalization. Pattern Recogn 39(5):969–979

    Article  Google Scholar 

  5. Boulgouris NV, Chi ZX (2007) Gait recognition using radon transform and linear discriminant analysis. IEEE Trans Image Process 16(3):731–740

    Article  MathSciNet  Google Scholar 

  6. Chakraborti T, McCane B, Mills S, Pal U (2018) Loop descriptor: local optimal-oriented pattern. IEEE Signal Process Lett 25(5):635–639

    Article  Google Scholar 

  7. Choudhury SD, Tjahjadi T (2015) Robust view-invariant multiscale gait recognition. Pattern Recogn 48(3):798–811

    Article  Google Scholar 

  8. Choudhury SD, Tjahjadi T (2016) Clothing and carrying condition invariant gait recognition based on rotation forest. Pattern Recogn Lett 80:1–7

    Article  Google Scholar 

  9. Cuntoor N, Kale A, Chellappa R (2003) Combining multiple evidences for gait recognition. In: ICME’03. Proceedings. 2003 International conference on multimedia and expo, 2003, vol 3. IEEE, pp III–113

  10. Deng M, Wang C (2018) Gait recognition under different clothing conditions via deterministic learning. IEEE/CAA Journal of Automatica Sinica

  11. Ding T (2008) A robust identification approach to gait recognition. In: 2008 IEEE Conference on computer vision and pattern recognition. IEEE, pp 1–8

  12. Dupuis Y, Savatier X, Vasseur P (2013) Feature subset selection applied to model-free gait recognition. Image Vis Comput 31(8):580–591

    Article  Google Scholar 

  13. El Gayar N, Schwenker F, Palm G (2006) A study of the robustness of knn classifiers trained using soft labels. In: IAPR Workshop on artificial neural networks in pattern recognition. Springer, pp 67–80

  14. Ghebleh A, Moghaddam ME (2018) Clothing-invariant human gait recognition using an adaptive outlier detection method. Multimed Tools Appl, 1–21

  15. Isaac ER, Elias S, Rajagopalan S, Easwarakumar K (2017) View-invariant gait recognition through genetic template segmentation. IEEE Signal Process Lett 24 (8):1188–1192

    Article  Google Scholar 

  16. Islam MS, Islam MR, Akter MS, Hossain M, Molla M (2013) Window based clothing invariant gait recognition. In: 2013 International conference on advances in electrical engineering (ICAEE). IEEE, pp 411–414

  17. Jabid T, Kabir MH, Chae O (2010) Gender classification using local directional pattern (ldp). In: 2010 20th International conference on pattern recognition (ICPR). IEEE, pp 2162–2165

  18. Kusakunniran W (2014) Recognizing gaits on spatio-temporal feature domain. IEEE Trans Inform Forens Secur 9(9):1416–1423

    Article  Google Scholar 

  19. Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn 44(4):973–987

    Article  Google Scholar 

  20. Lishani AO, Boubchir L, Khalifa E, Bouridane A (2018) Human gait recognition using gei-based local multi-scale feature descriptors. Multimed Tools Appl, 1–16

  21. Liu Z, Sarkar S (2004) Simplest representation yet for gait recognition: averaged silhouette. In: ICPR 2004. Proceedings of the 17th international conference on pattern recognition, 2004, vol 4. IEEE, pp 211–214

  22. Liu Z, Sarkar S (2006) Improved gait recognition by gait dynamics normalization. IEEE Trans Pattern Anal Mach Intell 28(6):863–876

    Article  Google Scholar 

  23. Liu Y, Collins R, Tsin Y (2002) Gait sequence analysis using frieze patterns. In: European conference on computer vision. Springer, pp 657–671

  24. Lo Presti L, La Cascia M (2015) Using Hankel matrices for dynamics-based facial emotion recognition and pain detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 26–33

  25. Lublinerman R, Ozay N, Zarpalas D, Camps O (2006) Activity recognition from silhouettes using linear systems and model (in) validation techniques. In: 18th International conference on pattern recognition (ICPR’06), vol 1. IEEE, pp 347–350

  26. Makihara Y, Mannami H, Tsuji A, Hossain MA, Sugiura K, Mori A, Yagi Y (2012) The ou-isir gait database comprising the treadmill dataset. IPSJ Trans Comput Vis Appl 4:53–62

    Article  Google Scholar 

  27. Man J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Article  Google Scholar 

  28. Muramatsu D, Makihara Y, Yagi Y (2016) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybern 46 (7):1602–1615

    Article  Google Scholar 

  29. Murase H, Sakai R (1996) Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recogn Lett 17(2):155–162

    Article  Google Scholar 

  30. Nandy A, Chakraborty R, Chakraborty P (2016) Cloth invariant gait recognition using pooled segmented statistical features. Neurocomputing 191:117–140

    Article  Google Scholar 

  31. Nandy A, Pathak A, Chakraborty P (2017) A study on gait entropy image analysis for clothing invariant human identification. Multimed Tools Appl 76 (7):9133–9167

    Article  Google Scholar 

  32. Ojala T, Pietikainen M, Harwood D (1994) Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: 1994. Vol. 1-conference a: computer vision & image processing., proceedings of the 12th IAPR international conference on pattern recognition, vol 1. IEEE, pp 582–585

  33. Phillips PJ, Sarkar S, Robledo I, Grother P, Bowyer K (2002) The gait identification challenge problem: data sets and baseline algorithm. In: Proceedings. 16th International conference on pattern recognition, 2002, vol 1. IEEE, pp 385–388

  34. Prakash C, Mittal A, Kumar R, Mittal N (2015) Identification of gait parameters from silhouette images. In: 2015 Eighth International conference on contemporary computing (IC3). IEEE, pp 190–195

  35. Rida I, Almaadeed S, Bouridane A (2016) Gait recognition based on modified phase-only correlation. Signal Image Vid Process 10(3):463–470

    Article  Google Scholar 

  36. Rida I, Jiang X, Marcialis GL (2016) Human body part selection by group lasso of motion for model-free gait recognition. IEEE Signal Process Lett 23(1):154–158

    Article  Google Scholar 

  37. Rokanujjaman M, Hossain MA, Islam MR, Islam MS (2013) Effective part definition for gait identification using gait entropy image. In: 2013 International conference on informatics, electronics & vision (ICIEV). IEEE, pp 1–4

  38. Semwal VB, Raj M, Nandi GC (2015) Biometric gait identification based on a multilayer perceptron. Robot Auton Syst 65:65–75

    Article  Google Scholar 

  39. Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2016) Geinet: view-invariant gait recognition using a convolutional neural network. In: 2016 international conference on biometrics (ICB). IEEE, pp 1–8

  40. Wang L, Tan T, Hu W, Ning H (2003) Automatic gait recognition based on statistical shape analysis. IEEE Trans Image Process 12(9):1120–1131

    Article  MathSciNet  Google Scholar 

  41. Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. In: European Conference on computer vision. Springer, pp 257–270

  42. Zhang S, Zhang L (2018) Combining weighted adaptive cs-lbp and local linear discriminant projection for gait recognition. Multimed Tools Appl 77(10):12331–12347

    Article  Google Scholar 

  43. Zhang Y, Yang N, Li W, Wu X, Ruan Q (2009) Gait recognition using procrustes shape analysis and shape context. In: Asian Conference on computer vision. Springer, pp 256–265

  44. Zheng S (2017) (accessed July 27, 2017) CASIA Gait Database. http://www.sinobiometrics.com

Download references

Acknowledgements

We convey our genuine gratitude to Prof Yasushi Yagi, Osaka University Japan and his whole research group for providing us, OU-ISIR Treadmill Gait database [26]. We thank the team behind The Institute of Automation, Chinese Academy of Sciences (CASIA) for sharing CASIA gait database [44], in the absence of which the experiments might not have been done. This work is supported by Visvesvaraya PhD Scheme, MeitY, Government of India.

Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, 575 025, India

    R. Anusha & C. D. Jaidhar

Authors
  1. R. Anusha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. D. Jaidhar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Anusha.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anusha, R., Jaidhar, C.D. Clothing invariant human gait recognition using modified local optimal oriented pattern binary descriptor. Multimed Tools Appl 79, 2873–2896 (2020). https://doi.org/10.1007/s11042-019-08400-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08400-8

Keywords

Search

Navigation